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planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/edger commit e646be741e315df9332b5206cec1e47c11370ff1
| author | iuc |
|---|---|
| date | Sun, 06 May 2018 13:38:41 -0400 |
| parents | a1634a9c2ee1 |
| children | 4730985c816f |
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<tool id="edger" name="edgeR" version="3.20.7.2"> <description> Perform differential expression of count data </description> <requirements> <requirement type="package" version="3.20.7">bioconductor-edger</requirement> <requirement type="package" version="3.34.9">bioconductor-limma</requirement> <requirement type="package" version="0.2.15">r-rjson</requirement> <requirement type="package" version="1.20.0">r-getopt</requirement> <requirement type="package" version="1.4.30">r-statmod</requirement> <!-- required for alpha function used with plotMD --> <requirement type="package" version="0.5.0">r-scales</requirement> </requirements> <version_command><![CDATA[ echo $(R --version | grep version | grep -v GNU)", edgeR version" $(R --vanilla --slave -e "library(edgeR); cat(sessionInfo()\$otherPkgs\$edgeR\$Version)" 2> /dev/null | grep -v -i "WARNING: ")", limma version" $(R --vanilla --slave -e "library(limma); cat(sessionInfo()\$otherPkgs\$limma\$Version)" 2> /dev/null | grep -v -i "WARNING: ")", scales version" $(R --vanilla --slave -e "library(scales); cat(sessionInfo()\$otherPkgs\$scales\$Version)" 2> /dev/null | grep -v -i "WARNING: ")", rjson version" $(R --vanilla --slave -e "library(rjson); cat(sessionInfo()\$otherPkgs\$rjson\$Version)" 2> /dev/null | grep -v -i "WARNING: ")", getopt version" $(R --vanilla --slave -e "library(getopt); cat(sessionInfo()\$otherPkgs\$getopt\$Version)" 2> /dev/null | grep -v -i "WARNING: ") ]]></version_command> <command detect_errors="exit_code"><![CDATA[ #import json Rscript '$__tool_directory__/edger.R' -R '$outReport' -o '$outReport.files_path' #if $input.format=="files": ## Adapted from DESeq2 wrapper #set $temp_factor_names = list() #for $fact in $input.rep_factor: #set $temp_factor = list() #for $g in $fact.rep_group: #set $count_files = list() #for $file in $g.countsFile: $count_files.append(str($file)) #end for $temp_factor.append( {str($g.groupName): $count_files} ) #end for $temp_factor.reverse() $temp_factor_names.append([str($fact.factorName), $temp_factor]) #end for -j '#echo json.dumps(temp_factor_names)#' #elif $input.format=="matrix": -m '$input.counts' #if $input.fact.ffile=='yes': -f '$input.fact.finfo' #else: -i '${ '|'.join( ['%s::%s' % ($x.factorName, $x.groupNames) for x in $input.fact.rep_factor] ) }' #end if #end if #if $anno.annoOpt=='yes': -a '$anno.geneanno' #end if -C '${ ','.join( ['%s' % $x.contrast for x in $rep_contrast] ) }' #if $f.filt.filt_select == 'yes': #if $f.filt.cformat.format_select == 'cpm': -c '$f.filt.cformat.cpmReq' -s '$f.filt.cformat.cpmSampleReq' #elif $f.filt.cformat.format_select == 'counts': -z '$f.filt.cformat.cntReq' #if $f.filt.cformat.samples.count_select == 'total': -y #elif $f.filt.cformat.samples.count_select == 'sample': -s '$f.filt.cformat.samples.cntSampleReq' #end if #end if #end if #if $out.normCounts: -x #end if #if $out.rdaOption: -r #end if -l '$adv.lfc' -p '$adv.pVal' -d '$adv.pAdjust' -n '$adv.normalisationOption' #if $adv.robOption: -b #end if #if $adv.lrtOption: -t #end if && mkdir ./output_dir && cp '$outReport.files_path'/*.tsv output_dir/ #if $out.rscript: && cp '$__tool_directory__/edger.R' '$rscript' #end if ]]></command> <inputs> <!-- Counts and Factors --> <conditional name="input"> <param name="format" type="select" label="Count Files or Matrix?" help="You can choose to input either separate count files (one per sample) or a single count matrix"> <option value="files">Separate Count Files</option> <option value="matrix">Single Count Matrix</option> </param> <when value="files"> <repeat name="rep_factor" title="Factor" min="1"> <param name="factorName" type="text" label="Name" help="Name of experiment factor of interest (e.g. Genotype). One factor must be entered and there must be two or more groups per factor. Optional additional factors (e.g. Batch) can be entered using the Insert Factor button below, see Help section for more information. NOTE: Please only use letters, numbers or underscores."> <sanitizer> <valid initial="string.letters,string.digits"><add value="_" /></valid> </sanitizer> </param> <repeat name="rep_group" title="Group" min="2" default="2"> <param name="groupName" type="text" label="Name" help="Name of group that the counts files belong to (e.g. WT or Mut). NOTE: Please only use letters, numbers or underscores (case sensitive)."> <sanitizer> <valid initial="string.letters,string.digits"><add value="_" /></valid> </sanitizer> </param> <param name="countsFile" type="data" format="tabular" multiple="true" label="Counts files"/> </repeat> </repeat> </when> <when value="matrix"> <param name="counts" type="data" format="tabular" label="Count Matrix"/> <conditional name="fact"> <param name="ffile" type="select" label="Input factor information from file?" help="You can choose to input the factor and group information for the samples from a file or manually enter below."> <option value="no">No</option> <option value="yes">Yes</option> </param> <when value="yes"> <param name="finfo" type="data" format="tabular" label="Factor File"/> </when> <when value="no" > <repeat name="rep_factor" title="Factor" min="1"> <param name="factorName" type="text" label="Factor Name" help="Name of experiment factor of interest (e.g. Genotype). One factor must be entered and there must be two or more groups per factor. Additional factors (e.g. Batch) can be entered using the Insert Factor button below, see Help section below. NOTE: Please only use letters, numbers or underscores."> <validator type="empty_field" /> <validator type="regex" message="Please only use letters, numbers or underscores">^[\w]+$</validator> </param> <param name="groupNames" type="text" label="Groups" help="Enter the group names for the samples separated with commas e.g. WT,WT,WT,Mut,Mut,Mut. The order of the names must match the order of the samples in the columns of the count matrix. NOTE: Please only use letters, numbers or underscores (case sensitive)."> <validator type="empty_field" /> <validator type="regex" message="Please only use letters, numbers or underscores, and separate levels by commas">^[\w,]+$</validator> </param> </repeat> </when> </conditional> </when> </conditional> <!-- Gene Annotations --> <conditional name="anno"> <param name="annoOpt" type="select" label="Use Gene Annotations?" help="If you provide an annotation file, annotations will be added to the table(s) of differential expression results to provide descriptions for each gene. See Help section below."> <option value="no">No</option> <option value="yes">Yes</option> </param> <when value="yes"> <param name="geneanno" type="data" format="tabular" label="Gene Annotations"/> </when> <when value="no" /> </conditional> <!-- Contrasts --> <repeat name="rep_contrast" title="Contrast" min="1" default="1"> <param name="contrast" type="text" label="Contrast of Interest" help="Names of two groups to compare separated by a hyphen e.g. Mut-WT. If the order is Mut-WT the fold changes in the results will be up/down in Mut relative to WT. If you have more than one contrast enter each separately using the Insert Contrast button below. For more info, see Chapter 8 in the limma User's guide: https://www.bioconductor.org/packages/release/bioc/vignettes/limma/inst/doc/usersguide.pdf"> <validator type="empty_field" /> <validator type="regex" message="Please only use letters, numbers or underscores">^[\w-]+$</validator> </param> </repeat> <!-- Filter Options --> <section name="f" expanded="false" title="Filter Low Counts"> <conditional name="filt"> <param name="filt_select" type="select" label="Filter lowly expressed genes?" help="Treat genes with very low expression as unexpressed and filter out. See the Filter Low Counts section below for more information. Default: No"> <option value="no" selected="true">No</option> <option value="yes">Yes</option> </param> <when value="yes"> <conditional name="cformat"> <param name="format_select" type="select" label="Filter on CPM or Count values?" help="It is slightly better to base the filtering on count-per-million (CPM) rather than the raw count values so as to avoid favoring genes expressed in samples sequenced to a higher depth. "> <option value="cpm">CPM</option> <option value="counts">Counts</option> </param> <when value="cpm"> <param name="cpmReq" type="float" value="1" min="0" label="Minimum CPM" help="Treat genes with CPM below this value as unexpressed and filter out. See the Filter Low Counts section below for more information."/> <param name="cpmSampleReq" type="integer" value="0" min="0" label="Minimum Samples" help="Filter out all genes that do not meet the Minimum CPM in at least this many samples. See the Filter Low Counts section below for more information."/> </when> <when value="counts"> <param name="cntReq" type="integer" value="0" min="0" label="Minimum Count" help="Filter out all genes that do not meet this minimum count. You can choose below to apply this filter to the total count for all samples or specify the number of samples under Minimum Samples. See the Filter Low Counts section below for more information." /> <conditional name="samples"> <param name="count_select" type="select" label="Filter on Total Count or per Sample Count values?" > <option value="total">Total</option> <option value="sample">Sample</option> </param> <when value="total"> <param name="totReq" type="boolean" truevalue="1" falsevalue="0" checked="false" label="Filter on Total Count" help="Apply the Minimum Count filter to genes after summing counts for all samples. See the Filter Low Counts section below for more information." /> </when> <when value="sample"> <param name="cntSampleReq" type="integer" value="0" min="0" label="Minimum Samples" help="Filter out all genes that do not meet the Minimum Count in at least this many samples. See the Filter Low Counts section below for more information."/> </when> </conditional> </when> </conditional> </when> <when value="no" /> </conditional> </section> <!-- Output Options --> <section name="out" expanded="false" title="Output Options"> <param name="normCounts" type="boolean" truevalue="1" falsevalue="0" checked="false" label="Output Normalised Counts Table?" help="Output a file containing the normalised counts, these are in log2 counts per million (logCPM). Default: No"> </param> <param name="rscript" type="boolean" truevalue="True" falsevalue="False" checked="False" label="Output Rscript?" help="If this option is set to Yes, the Rscript used will be provided as a text file in the output. Default: No"/> <param name="rdaOption" type="boolean" truevalue="1" falsevalue="0" checked="false" label="Output RData file?" help="Output all the data used by R to construct the plots and tables, can be loaded into R. A link to the RData file will be provided in the HTML report. Default: No"> </param> </section> <!-- Advanced Options --> <section name="adv" expanded="false" title="Advanced Options"> <param name="lfc" type="float" value="0" min="0" label="Minimum Log2 Fold Change" help="Genes above this threshold and below the p-value threshold are considered significant and highlighted in the MD plot. Default: 0."/> <param name="pVal" type="float" value="0.05" min="0" max="1" label="P-Value Adjusted Threshold" help="Genes below this threshold are considered significant and highlighted in the MD plot. If either BH(1995) or BY(2001) are selected then this value is a false-discovery-rate control. If Holm(1979) is selected then this is an adjusted p-value for family-wise error rate. Default: 0.05."/> <param name="pAdjust" type="select" label="P-Value Adjustment Method" help="Default: BH"> <option value="BH" selected="true">Benjamini and Hochberg (1995)</option> <option value="BY">Benjamini and Yekutieli (2001)</option> <option value="holm">Holm (1979)</option> <option value="none">None</option> </param> <param name="normalisationOption" type="select" label="Normalisation Method" help="Default: TMM"> <option value="TMM" selected="true">TMM</option> <option value="RLE">RLE</option> <option value="upperquartile">Upperquartile</option> <option value="none">None (Don't normalise)</option> </param> <param name="robOption" type="boolean" truevalue="1" falsevalue="0" checked="true" label="Use Robust Settings?" help="Using robust settings is usually recommended to protect against outlier genes. Default: Yes" /> <param name="lrtOption" type="boolean" truevalue="1" falsevalue="0" checked="false" label="Use Likelihood Ratio Test?" help="Use likelihood ratio test instead of quasi-likelihood F-test. Default: No"/> </section> </inputs> <outputs> <data name="outReport" format="html" label="${tool.name} on ${on_string}: Report" /> <collection name="outTables" type="list" label="${tool.name} on ${on_string}: Tables"> <discover_datasets pattern="(?P<name>.+)\.tsv$" format="tabular" directory="output_dir" visible="false" /> </collection> <data name="rscript" format="txt" label="${tool.name} on ${on_string}: Rscript"> <filter>out['rscript']</filter> </data> </outputs> <tests> <!-- Ensure report is output --> <test> <param name="format" value="matrix" /> <param name="counts" value="matrix.txt" /> <repeat name="rep_factor"> <param name="factorName" value="Genotype"/> <param name="groupNames" value="Mut,Mut,Mut,WT,WT,WT" /> </repeat> <repeat name="rep_contrast"> <param name="contrast" value="Mut-WT" /> </repeat> <repeat name="rep_contrast"> <param name="contrast" value="WT-Mut" /> </repeat> <param name="normalisationOption" value="TMM" /> <output_collection name="outTables" count="2"> <element name="edgeR_Mut-WT" ftype="tabular" > <assert_contents> <has_text_matching expression="GeneID.*logFC.*logCPM.*F.*PValue.*FDR" /> <has_text_matching expression="11304.*0.4582" /> </assert_contents> </element> <element name="edgeR_WT-Mut" ftype="tabular" > <assert_contents> <has_text_matching expression="GeneID.*logFC.*logCPM.*F.*PValue.*FDR" /> <has_text_matching expression="11304.*-0.4582" /> </assert_contents> </element> </output_collection> <output name="outReport" > <assert_contents> <has_text text="edgeR Analysis Output" /> <has_text text="quasi-likelihood" /> <not_has_text text="likelihood ratio" /> <not_has_text text="RData" /> </assert_contents> </output> </test> <!-- Ensure annotation file input works --> <test> <param name="format" value="matrix" /> <param name="annoOpt" value="yes" /> <param name="geneanno" value="anno.txt" /> <param name="counts" value="matrix.txt" /> <repeat name="rep_factor"> <param name="factorName" value="Genotype"/> <param name="groupNames" value="Mut,Mut,Mut,WT,WT,WT" /> </repeat> <repeat name="rep_contrast"> <param name="contrast" value="Mut-WT" /> </repeat> <param name="normalisationOption" value="TMM" /> <output_collection name="outTables" count="1"> <element name="edgeR_Mut-WT" ftype="tabular" > <assert_contents> <has_text_matching expression="EntrezID.*Symbol.*logFC.*logCPM.*F.*PValue.*FDR" /> <has_text_matching expression="11304.*Abca4.*0.4582" /> </assert_contents> </element> </output_collection> </test> <!-- Ensure RScript and RData file can be output --> <test> <param name="format" value="matrix" /> <param name="rscript" value="True"/> <param name="rdaOption" value="true" /> <param name="counts" value="matrix.txt" /> <repeat name="rep_factor"> <param name="factorName" value="Genotype"/> <param name="groupNames" value="Mut,Mut,Mut,WT,WT,WT" /> </repeat> <repeat name="rep_contrast"> <param name="contrast" value="Mut-WT" /> </repeat> <param name="normalisationOption" value="TMM" /> <output name="outReport" > <assert_contents> <has_text text="RData" /> </assert_contents> </output> <output name="rscript" value="out_rscript.txt"/> </test> <!-- Ensure secondary factors work --> <test> <param name="format" value="matrix" /> <param name="counts" value="matrix.txt" /> <repeat name="rep_factor"> <param name="factorName" value="Genotype"/> <param name="groupNames" value="Mut,Mut,Mut,WT,WT,WT" /> </repeat> <repeat name="rep_factor"> <param name="factorName" value="Batch"/> <param name="groupNames" value="b1,b2,b3,b1,b2,b3"/> </repeat> <repeat name="rep_contrast"> <param name="contrast" value="Mut-WT" /> </repeat> <param name="normalisationOption" value="TMM" /> <output_collection name="outTables" count="1" > <element name="edgeR_Mut-WT" ftype="tabular" > <assert_contents> <has_text_matching expression="GeneID.*logFC.*logCPM.*F.*PValue.*FDR" /> <has_text_matching expression="11304.*0.4584" /> </assert_contents> </element> </output_collection> </test> <!-- Ensure factors file input works --> <test> <param name="format" value="matrix" /> <param name="ffile" value="yes" /> <param name="finfo" value="factorinfo.txt" /> <param name="counts" value="matrix.txt" /> <repeat name="rep_contrast"> <param name="contrast" value="Mut-WT" /> </repeat> <param name="normalisationOption" value="TMM" /> <output_collection name="outTables" count="1"> <element name="edgeR_Mut-WT" ftype="tabular" > <assert_contents> <has_text_matching expression="GeneID.*logFC.*logCPM.*F.*PValue.*FDR" /> <has_text_matching expression="11304.*0.4584" /> </assert_contents> </element> </output_collection> </test> <!-- Ensure normalised counts file output works--> <test> <param name="format" value="matrix" /> <param name="normCounts" value="true" /> <param name="counts" value="matrix.txt" /> <repeat name="rep_factor"> <param name="factorName" value="Genotype"/> <param name="groupNames" value="Mut,Mut,Mut,WT,WT,WT" /> </repeat> <repeat name="rep_contrast"> <param name="contrast" value="Mut-WT" /> </repeat> <param name="normalisationOption" value="TMM" /> <output_collection name="outTables" count="2"> <element name="edgeR_Mut-WT" ftype="tabular" > <assert_contents> <has_text_matching expression="GeneID.*logFC.*logCPM.*F.*PValue.*FDR" /> <has_text_matching expression="11304.*0.4582" /> </assert_contents> </element> <element name="edgeR_normcounts" ftype="tabular" > <assert_contents> <has_text_matching expression="GeneID.*Mut1.*Mut2.*Mut3.*WT1.*WT2.*WT3" /> <has_text_matching expression="11304.*15.7535" /> </assert_contents> </element> </output_collection> </test> <!-- Ensure likelihood ratio option works --> <test> <param name="format" value="matrix" /> <param name="counts" value="matrix.txt" /> <repeat name="rep_factor"> <param name="factorName" value="Genotype"/> <param name="groupNames" value="Mut,Mut,Mut,WT,WT,WT" /> </repeat> <repeat name="rep_contrast"> <param name="contrast" value="Mut-WT" /> </repeat> <param name="normalisationOption" value="TMM" /> <param name="lrtOption" value="true" /> <output name="outReport" > <assert_contents> <has_text text="likelihood ratio" /> <not_has_text text="quasi-likelihood" /> </assert_contents> </output> </test> <!-- Ensure multiple counts files input works --> <test> <param name="format" value="files" /> <repeat name="rep_factor"> <param name="factorName" value="Genotype"/> <repeat name="rep_group"> <param name="groupName" value="WT"/> <param name="countsFile" value="WT1.counts,WT2.counts,WT3.counts"/> </repeat> <repeat name="rep_group"> <param name="groupName" value="Mut"/> <param name="countsFile" value="Mut1.counts,Mut2.counts,Mut3.counts"/> </repeat> </repeat> <repeat name="rep_factor"> <param name="factorName" value="Batch"/> <repeat name="rep_group"> <param name="groupName" value="b1"/> <param name="countsFile" value="WT1.counts,Mut1.counts"/> </repeat> <repeat name="rep_group"> <param name="groupName" value="b2"/> <param name="countsFile" value="WT2.counts,Mut2.counts"/> </repeat> <repeat name="rep_group"> <param name="groupName" value="b3"/> <param name="countsFile" value="WT3.counts,Mut3.counts"/> </repeat> </repeat> <param name="annoOpt" value="yes" /> <param name="geneanno" value="anno.txt" /> <repeat name="rep_contrast"> <param name="contrast" value="Mut-WT" /> </repeat> <repeat name="rep_contrast"> <param name="contrast" value="WT-Mut" /> </repeat> <param name="normCounts" value="true" /> <output_collection name="outTables" count="3"> <element name="edgeR_Mut-WT" ftype="tabular" > <assert_contents> <has_text_matching expression="EntrezID.*Symbol.*logFC.*logCPM.*F.*PValue.*FDR" /> <has_text_matching expression="11304.*Abca4.*0.4584" /> </assert_contents> </element> <element name="edgeR_WT-Mut" ftype="tabular" > <assert_contents> <has_text_matching expression="logFC.*logCPM.*F.*PValue.*FDR" /> <has_text_matching expression="11304.*Abca4.*-0.4584" /> </assert_contents> </element> <element name="edgeR_normcounts" ftype="tabular" > <assert_contents> <has_text_matching expression="Mut1.*Mut2.*Mut3.*WT1.*WT2.*WT3" /> <has_text_matching expression="11304.*Abca4.*15.7535" /> </assert_contents> </element> </output_collection> </test> <!-- Ensure filtering on CPM in Mnimum Samples works --> <test> <param name="format" value="matrix" /> <param name="counts" value="matrix.txt" /> <repeat name="rep_factor"> <param name="factorName" value="Genotype"/> <param name="groupNames" value="Mut,Mut,Mut,WT,WT,WT" /> </repeat> <repeat name="rep_contrast"> <param name="contrast" value="Mut-WT" /> </repeat> <param name="normalisationOption" value="TMM" /> <param name="filt_select" value="yes" /> <param name="format_select" value="cpm" /> <!-- real cpmReq values would be a lot lower this is just for this tiny test dataset --> <param name="cpmReq" value="1000" /> <param name="cpmSampleReq" value="3" /> <output name="outReport" > <assert_contents> <has_text text="CPM in at least" /> <not_has_text text="after summing counts for all samples" /> <not_has_text text="counts in at least" /> </assert_contents> </output> <output_collection name="outTables" count="1" > <element name="edgeR_Mut-WT" ftype="tabular" > <assert_contents> <has_text_matching expression="GeneID.*logFC.*logCPM.*F.*PValue.*FDR" /> <has_text_matching expression="11304.*0.4568" /> <not_has_text text="-0.0682" /> </assert_contents> </element> </output_collection> </test> <!-- Ensure filtering on Count in Minmum Samples works --> <test> <param name="format" value="matrix" /> <param name="counts" value="matrix.txt" /> <repeat name="rep_factor"> <param name="factorName" value="Genotype"/> <param name="groupNames" value="Mut,Mut,Mut,WT,WT,WT" /> </repeat> <repeat name="rep_contrast"> <param name="contrast" value="Mut-WT" /> </repeat> <param name="normalisationOption" value="TMM" /> <param name="filt_select" value="yes" /> <param name="format_select" value="counts" /> <param name="cntReq" value="10" /> <param name="count_select" value="sample" /> <param name="cntSampleReq" value="3" /> <output name="outReport" > <assert_contents> <has_text text="counts in at least" /> <not_has_text text="after summing counts for all samples" /> <not_has_text text="CPM in at least" /> </assert_contents> </output> <output_collection name="outTables" count="1" > <element name="edgeR_Mut-WT" ftype="tabular" > <assert_contents> <has_text_matching expression="GeneID.*logFC.*logCPM.*F.*PValue.*FDR" /> <has_text_matching expression="11304.*0.4568" /> <not_has_text text="-0.0682" /> </assert_contents> </element> </output_collection> </test> <!-- Ensure filtering on Total Count works --> <test> <param name="format" value="matrix" /> <param name="counts" value="matrix.txt" /> <repeat name="rep_factor"> <param name="factorName" value="Genotype"/> <param name="groupNames" value="Mut,Mut,Mut,WT,WT,WT" /> </repeat> <repeat name="rep_contrast"> <param name="contrast" value="Mut-WT" /> </repeat> <param name="normalisationOption" value="TMM" /> <param name="filt_select" value="yes" /> <param name="format_select" value="counts" /> <!-- real cntReq values would be a lot lower this is just for this tiny test dataset --> <param name="cntReq" value="1000" /> <param name="count_select" value="total" /> <param name="totReq" value="true" /> <output name="outReport" > <assert_contents> <has_text text="after summing counts for all samples" /> <not_has_text text="counts in at least" /> <not_has_text text="CPM in at least" /> </assert_contents> </output> <output_collection name="outTables" count="1" > <element name="edgeR_Mut-WT" ftype="tabular" > <assert_contents> <has_text_matching expression="GeneID.*logFC.*logCPM.*F.*PValue.*FDR" /> <has_text_matching expression="11304.*0.4568" /> <not_has_text text="-0.0682" /> </assert_contents> </element> </output_collection> </test> </tests> <help><![CDATA[ .. class:: infomark **What it does** Given a counts matrix, or a set of counts files, for example from **featureCounts**, and optional information about the genes, this tool produces plots and tables useful in the analysis of differential gene expression. This tool uses the `edgeR`_ quasi-likelihood pipeline (edgeR-quasi) for differential expression analysis. This statistical methodology uses negative binomial generalized linear models, but with F-tests instead of likelihood ratio tests. This method provides stricter error rate control than other negative binomial based pipelines, including the traditional edgeR pipelines or DESeq2. While the limma pipelines are recommended for large-scale datasets, because of their speed and flexibility, the edgeR-quasi pipeline gives better performance in low-count situations. For the data analyzed in this `edgeR workflow article`_ ,the edgeR-quasi, limma-voom and limma-trend pipelines are all equally suitable and give similar results. .. _edgeR: http://www.bioconductor.org/packages/release/bioc/html/edgeR.html .. _edgeR workflow article: https://f1000research.com/articles/5-1438 ----- **Inputs** **Counts Data:** The counts data can either be input as separate counts files (one sample per file) or a single count matrix (one sample per column). The rows correspond to genes, and columns correspond to the counts for the samples. Values must be tab separated, with the first row containing the sample/column labels and the first column containing the row/gene labels. Gene identifiers can be of any type but must be unique and not repeated within a counts file. Example - **Separate Count Files**: ========== ======= **GeneID** **WT1** ---------- ------- 11287 1699 11298 1905 11302 6 11303 2099 11304 356 11305 2528 ========== ======= Example - **Single Count Matrix**: ========== ======= ======= ======= ======== ======== ======== **GeneID** **WT1** **WT2** **WT3** **Mut1** **Mut2** **Mut3** ---------- ------- ------- ------- -------- -------- -------- 11287 1699 1528 1601 1463 1441 1495 11298 1905 1744 1834 1345 1291 1346 11302 6 8 7 5 6 5 11303 2099 1974 2100 1574 1519 1654 11304 356 312 337 361 397 346 11305 2528 2438 2493 1762 1942 2027 ========== ======= ======= ======= ======== ======== ======== **Factor Information:** Enter factor names and groups in the tool form, or provide a tab-separated file that has the samples in the same order as listed in the columns of the counts matrix. The second column should contain the primary factor levels (e.g. WT, Mut) with optional additional columns for any secondary factors. Example: ========== ============ ========= **Sample** **Genotype** **Batch** ---------- ------------ --------- WT1 WT b1 WT2 WT b2 WT3 WT b3 Mut1 Mut b1 Mut2 Mut b2 Mut3 Mut b3 ========== ============ ========= *Factor Name:* The name of the experimental factor being investigated e.g. Genotype, Treatment. One factor must be entered and spaces must not be used. Optionally, additional factors can be included, these are variables that might influence your experiment e.g. Batch, Gender, Subject. If additional factors are entered, edgeR will fit an additive linear model. *Groups:* The names of the groups for the factor. These must be entered in the same order as the samples (to which the groups correspond) are listed in the columns of the counts matrix. Spaces must not be used and if entered into the tool form above, the values should be separated by commas. **Gene Annotations:** Optional input for gene annotations, this can contain more information about the genes than just an ID number. The annotations will be available in the differential expression results table and the optional normalised counts table. The file must contain a header row and have the gene IDs in the first column. The number of rows should match that of the counts files, add NA for any gene IDs with no annotation. The Galaxy tool **annotateMyIDs** can be used to obtain annotations for human, mouse, fly and zebrafish. Example: ========== ========== =================================================== **GeneID** **Symbol** **GeneName** ---------- ---------- --------------------------------------------------- 11287 Pzp pregnancy zone protein 11298 Aanat arylalkylamine N-acetyltransferase 11302 Aatk apoptosis-associated tyrosine kinase 11303 Abca1 ATP-binding cassette, sub-family A (ABC1), member 1 11304 Abca4 ATP-binding cassette, sub-family A (ABC1), member 4 11305 Abca2 ATP-binding cassette, sub-family A (ABC1), member 2 ========== ========== =================================================== **Contrasts of Interest:** The contrasts you wish to make between levels. A common contrast would be a simple difference between two levels: "Mut-WT" represents the difference between the mutant and wild type genotypes. Multiple contrasts must be entered separately using the Insert Contrast button, spaces must not be used. **Filter Low Counts:** Genes with very low counts across all libraries provide little evidence for differential expression. In the biological point of view, a gene must be expressed at some minimal level before it is likely to be translated into a protein or to be biologically important. In addition, the pronounced discreteness of these counts interferes with some of the statistical approximations that are used later in the pipeline. These genes should be filtered out prior to further analysis. As a rule of thumb, genes are dropped if they can’t possibly be expressed in all the samples for any of the conditions. Users can set their own definition of genes being expressed. Usually a gene is required to have a count of 5-10 in a library to be considered expressed in that library. Users should also filter with count-per-million (CPM) rather than filtering on the counts directly, as the latter does not account for differences in library sizes between samples. Option to ignore the genes that do not show significant levels of expression, this filtering is dependent on two criteria: CPM/count and number of samples. You can specify to filter on CPM (Minimum CPM) or count (Minimum Count) values: * **Minimum CPM:** This is the minimum count per million that a gene must have in at least the number of samples specified under Minimum Samples. * **Minimum Count:** This is the minimum count that a gene must have. It can be combined with either Filter on Total Count or Minimum Samples. * **Filter on Total Count:** This can be used with the Minimum Count filter to keep genes with a minimum total read count. * **Minimum Samples:** This is the number of samples in which the Minimum CPM/Count requirement must be met in order for that gene to be kept. If the Minimum Samples filter is applied, only genes that exhibit a CPM/count greater than the required amount in at least the number of samples specified will be used for analysis. Care should be taken to ensure that the sample requirement is appropriate. In the case of an experiment with two experimental groups each with two members, if there is a change from insignificant CPM/count to significant CPM/count but the sample requirement is set to 3, then this will cause that gene to fail the criteria. When in doubt simply do not filter or consult the `edgeR workflow article`_ for filtering recommendations. **Advanced Options:** By default error rate for multiple testing is controlled using Benjamini and Hochberg's false discovery rate control at a threshold value of 0.05. However there are options to change this to custom values. * **Minimum log2-fold-change Required:** In addition to meeting the requirement for the adjusted statistic for multiple testing, the observation must have an absolute log2-fold-change greater than this threshold to be considered significant, thus highlighted in the MD plot. * **Adjusted Threshold:** Set the threshold for the resulting value of the multiple testing control method. Only observations whose statistic falls below this value is considered significant, thus highlighted in the MD plot. * **P-Value Adjustment Method:** Change the multiple testing control method, the options are BH(1995) and BY(2001) which are both false discovery rate controls. There is also Holm(1979) which is a method for family-wise error rate control. **Normalisation Method:** The most obvious technical factor that affects the read counts, other than gene expression levels, is the sequencing depth of each RNA sample. edgeR adjusts any differential expression analysis for varying sequencing depths as represented by differing library sizes. This is part of the basic modeling procedure and flows automatically into fold-change or p-value calculations. It is always present, and doesn’t require any user intervention. The second most important technical influence on differential expression is one that is less obvious. RNA-seq provides a measure of the relative abundance of each gene in each RNA sample, but does not provide any measure of the total RNA output on a per-cell basis. This commonly becomes important when a small number of genes are very highly expressed in one sample, but not in another. The highly expressed genes can consume a substantial proportion of the total library size, causing the remaining genes to be under-sampled in that sample. Unless this RNA composition effect is adjusted for, the remaining genes may falsely appear to be down-regulated in that sample . The edgeR `calcNormFactors` function normalizes for RNA composition by finding a set of scaling factors for the library sizes that minimize the log-fold changes between the samples for most genes. The default method for computing these scale factors uses a trimmed mean of M values (TMM) between each pair of samples. We call the product of the original library size and the scaling factor the *effective library size*. The effective library size replaces the original library size in all downsteam analyses. TMM is the recommended method for most RNA-Seq data where the majority (more than half) of the genes are believed not differentially expressed between any pair of the samples. You can change the normalisation method under **Advanced Options** above. For more information, see the `calcNormFactors` section in the `edgeR User's Guide`_. **Robust Settings** Option to use robust settings. Using robust settings (robust=TRUE) with the edgeR estimateDisp and glmQLFit functions is usually recommended to protect against outlier genes. This is turned on by default. Note that it is only used with the quasi-likelihood F test method. For more information, see the `edgeR workflow article`_. **Test Method** Option to use the likelihood ratio test instead of the quasi-likelihood F test. For more information, see the `edgeR workflow article`_. .. _edgeR User's Guide: http://www.bioconductor.org/packages/release/bioc/html/edgeR.html ----- **Outputs** This tool outputs * a table of differentially expressed genes for each contrast of interest * a HTML report with plots and additional information Optionally, under **Output Options** you can choose to output * a normalised counts table * the R script used by this tool * an RData file ----- **Citations** Please try to cite the appropriate articles when you publish results obtained using software, as such citation is the main means by which the authors receive credit for their work. For the edgeR method itself, please cite Robinson et al., 2010, and for this tool (which was developed from the Galaxy limma-voom tool) please cite Liu et al., 2015. ]]></help> <citations> <citation type="doi">10.1093/bioinformatics/btp616</citation> <citation type="doi">10.1093/nar/gkv412</citation> </citations> </tool>